How to create and annotate a dataset for AI? All you need to know
Introduction: what is a data set, and what is its importance for artificial intelligence?
Today, we are going to discuss an essential but often underestimated step in the development process: the creation and collection of datasets for Artificial Intelligence (AI). Whether you are a data professional or an AI fan, this guide aims to provide you with practical tips for building a solid and reliable dataset.
Machine Learning (ML), an essential branch of Artificial Intelligence, depends heavily on the quality of the initial datasets used in development cycles. Having enough data adapted for specific Machine Learning applications is fundamental. This article will give you an overview of best practices for creating datasets for machine learning and their use for specific tasks. You will understand what is required to collect and generate the right data for each machine learning algorithm.
💡 Remember, AI is based on 3 pillars: Datasets, Computing Power (GPU/TPU) and Models. Discover in following this link how to evaluate a machine learning model.
1. Understand the importance of a quality dataset for AI
Any AI project depends heavily on the data quality upon which the underlying model is trained. A well-designed dataset is to AI what good ingredients are to a chef: essential for exceptional results. A machine learning dataset is in fact a data set used to train an ML model. Creating a good dataset is therefore a critical step in the process of training and evaluating ML models. It is important to understand how to generate data for machine learning and to determine what data is needed to create a complete and effective dataset.
In practice, a dataset is:
- A collection of consistent data which can take various formats (texts, numbers, images, videos, etc.).
- A set where each value is associated with an attribute and an observation, for example, data on individuals with attributes such as age, weight, address, etc.
- A coherent set, which have been subject to checks to ensure the validity of data sources, to avoid working with data that is inaccurate, biased, or does not comply with intellectual property rules.
A dataset is not:
- A simple random assembly of data : datasets must be structured and organized in a logical and coherent way.
- Exempt from quality control : the verification and validation of data are essential to ensure their reliability.
- Still usable in its original condition : often, data cleaning and transformation are necessary before use.
- An infallible source : even the best datasets can contain errors, quality issues, or biases that require analysis and correction.
- A static set : A good dataset may require updates and revisions to remain relevant and useful.
The quality and size of a dataset play a decisive role in the accuracy and performance of the AI model. In general, the more reliable and high-quality data a model has, the better its performance will be. However, it is important to find a balance between the amount of data stored for processing and the human and IT resources required to process it.
2. Define the purpose of your dataset
Before you start building a dataset, that is to say before diving into the laborious phase of data collection, clarify the purpose of your AI. What are you looking to achieve? This definition will guide your choices in terms of the types and volume of data needed.
Obtaining data: should you use an existing dataset, synthetic data or collect data?
When initiating an AI development without having data, it is useful to turn to public open source datasets. These datasets, coming from Open Source communities or public organizations, offer a wide range of information useful for certain use cases.
Sometimes, data scientists turn to synthetic data. What is it about? This is data that is artificially generated, often using algorithms, to simulate real data. They are used in various fields for training and validating models when real data is insufficient, expensive to obtain, or to maintain confidentiality. This data mimics the statistical characteristics of real data, allowing AI models to be tested and refined in a controlled environment. However, it is preferable to use real data to avoid a discrepancy between the characteristics of synthetic data and real data (these differences are also called “distortions”). Although practical and relatively simple to obtain, synthetic data can make machine learning models less accurate or less efficient when applied to real situations.
The importance of data quality...
Although public datasets or synthetic data can provide Insights precious, the collecting your own data, adapted to your specific needs, is often more advantageous. Whatever the source of your data, there is one constant: the data quality and the need to label them correctly to provide them with a layer of semantic information are important aspects to consider for your work in the field of AI.
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3. Data collection: a strategic step in the AI development process
La training data collection is a critical step in the AI development process. The more thorough and rigorous you are during this stage, the more effective the ML algorithm will be. Thus, collecting as much relevant data as possible, while balancing their diversity, representativeness, and your hardware and software capabilities, is a top job, although often overlooked.
When building and optimizing your machine learning models, your strategy should be to use your own data. This data is naturally adapted to your specific needs and represent the best way to optimize your model for the types of data it will encounter in real life situations. Depending on how old your business is, you should have this data internally, in Data Lakes at best, or in various structured and unstructured databases collected over the years.
While obtaining data internally is one of the best approaches, unlike multinationals, the smallest structures (especially startups), do not always have at their disposal data sets built by thousands of employees. So you have to be inventive, and imagine other ways to get the data. Here are two methods that have been proven to work:
The”Crawling“and the”Scrapping“
- The “crawling” consists of browsing a large number of web pages that may be of interest to you.
- The”Scrapping“is the process of collecting data from these pages.
These tasks, which can vary in complexity, allow the collection of various types of datasets such as plain text, introductory texts for specific models, text with metadata for classification models, multilingual text for translation models, and images with legends for training models of image classification or image to text conversion.
Use datasets distributed by researchers
It is likely that other researchers have already been interested in problems similar to yours. In this case, it is possible to find and use the datasets that they created or used. If these datasets are freely available on an open source platform, you can retrieve them directly. If not, feel free to contact the researchers to see if they agree to share their data.
4. Data cleaning and preparation
This step consists of check your dataset to eliminate errors, duplicates, and structure it. A clean dataset is essential for effective AI learning.
Format, clean, and reduce data
To create a quality dataset, there are three key steps:
- Data formatting, which consists of carrying out checks to ensure the consistency of the data. For example, is the date format in your data the same for each entry?
- Data cleaning, which involves the elimination of missing, erroneous, or unrepresentative values to improve the accuracy of the algorithm.
- Data reduction, which consists of reducing the size of the dataset by removing irrelevant or least relevant information.
These steps are essential to obtain a useful and optimized dataset in Machine Learning.
Preparing the data
Datasets often have flaws that can affect the accuracy and performance of machine learning models. Common problems include Class imbalance (one class predominant over another), The missing data (compromising the accuracy and generalization of the model), The “noise” (incorrect or irrelevant information, such as images that are too blurry) and Outliers (very high or very low, distorting the results). To address these issues, Data Scientists need to clean and prepare data in advance to ensure the reliability and effectiveness of the model.
Increase in data
The ”Data Augmentation“ is a key machine learning technique for enriching a data set. It consists in creating new data from existing data through various transformations. For example, in image processing, this may involve changing the lighting, rotating, or zooming in on an image. This method increases the diversity of data, allowing an AI model to learn from more varied examples, and thus improves its ability to generalize to new situations.
Above all, increasing data sets is a smart way to increase the amount of training data without having to collect new real data.
5. Annotation: the language of your data
Annotating a dataset is assign labels to data to make it interpretable by AI, an operation that requires rigor and precision because it directly influences the algorithm's decision-making, i.e. how the AI will process the data. This task can be greatly facilitated by the use of dedicated annotation platforms such as Kili, V7 Labs or Label Studio. These tools offer intuitive interfaces and advanced features for accurate annotation, thus contributing to the efficiency and accuracy of machine learning models.
Data annotation for AI generally involves a human expertise to accurately label data, an essential step in training models. The more complex or specific your datasets are or require training in particular rules or mechanisms, the more the human expertise of Data Labelers becomes necessary. With technological advances, annotation capabilities are increasingly complemented by automated tools. These tools use algorithms to pre-annotate data, thus reducing the time and effort required for manual annotation, while requiring human verification and validation to ensure the accuracy and relevance of assigned labels. The latest updates to labeling platforms on the market offer advanced automatic selection or review functionalities, which make the annotation work less and less laborious for annotators. Thanks to these tools, Data Labeling is becoming a profession in its own right.
6. Optimizing a dataset: testing and iterating
After collecting and annotating a large amount of data, the next logical step is to test your dataset to assess the performance of your AI model. Therefore, it is aboutan iterative approach, and you will have to go back to the previous steps to improve the quality of the data or the labels produced.
For Evaluate the quality of a dataset, here are some questions you can ask yourself:
- Are the data representative of the population or phenomenon studied?
- Was data collection done ethically and legally?
- Is the data varied enough to cover different use cases?
- Was data quality affected during the collection and annotation cycle, for example during the transfer or storage process?
- Does the data contain biases or errors that could influence model results?
- Are there unexpected dependencies or correlations between variables?
These questions will help you thoroughly assess the quality of your data to ensure the efficiency and reliability of your AI models.
In conclusion...
We are coming to the end of this article. You will have understood it: creating and annotating a dataset are fundamental steps in the development of AI solutions. By following our advice, we hope you can lay the solid foundations needed to train efficient and reliable AI models. Good luck with your experiments and projects, and don't forget: a good dataset is the key to the success of your AI project !
Finally, we thought of you by bringing together A list of the top 10 sites for finding machine learning datasets. If this list seems incomplete or if you have more specific data needs, our team is at your disposal to assist you in collecting and annotating personalized, high-quality datasets. Do not hesitate to use our services to refine your Machine Learning projects.
Our top 10 sites where to find datasets for Machine Learning
- Kaggle dataset: 🔗 https://www.kaggle.com/datasets
- Hugging Face datasets: 🔗 https://huggingface.co/docs/datasets/index
- Amazon Datasets: 🔗 https://registry.opendata.aws
- Google datasets search engine: 🔗 https://datasetsearch.research.google.com
- Platform for the dissemination of public data from the French State: 🔗 https://data.gouv.fr
- European Union Open Data Portal: 🔗 http://data.europa.eu/euodp
- Reddit community datasets: 🔗 https://www.reddit.com/r/datasets
- UCI Machine Learning Repository: 🔗 https://archive.ics.uci.edu
- INSEE website: 🔗 https://www.insee.fr/fr/information/2410988
- Nasa platform: 🔗 https://data.nasa.gov
(BONUS) - SDSC, platform for providing annotated data for medical use cases: 🔗 https://www.surgicalvideo.io/
